Investigative Data Mining for Security and Criminal Detection is the first book to outline how data mining technologies can be used to combat crime in the 21st century. It introduces security managers, law enforcement investigators, counter-intelligence agents, fraud specialists, and information security analysts to the latest data mining techniques and shows how they can be used as investigative tools. Readers will learn how to search public and private databases and networks to flag potential security threats and root out criminal activities even before they occur.
The groundbreaking book reviews the latest data mining technologies including intelligent agents, link analysis, text mining, decision trees, self-organizing maps, machine learning, and neural networks. Using clear, understandable language, it explains the application of these technologies in such areas as computer and network security, fraud prevention, law enforcement, and national defense. International case studies throughout the book further illustrate how these technologies can be used to aid in crime prevention.
Investigative Data Mining for Security and Criminal Detection will also serve as an indispensable resource for software developers and vendors as they design new products for the law enforcement and intelligence communities.

Key Features:
* Covers cutting-edge data mining technologies available to use in evidence gathering and collection
* Includes numerous case studies, diagrams, and screen captures to illustrate real-world applications of data mining
* Easy-to-read format illustrates current and future data mining uses in preventative law enforcement, criminal profiling, counter-terrorist initiatives, and forensic science
* Introduces cutting-edge technologies in evidence gathering and collection, using clear non-technical language
* Illustrates current and future applications of data mining tools in preventative law enforcement, homeland security, and other areas of crime detection and prevention
* Shows how to construct predictive models for detecting criminal activity and for behavioral profiling of perpetrators
* Features numerous Web links, vendor resources, case studies, and screen captures illustrating the use of artificial intelligence (AI) technologies

Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. Machine Learning Forensics for Law Enforcement, Security, and Intelligence integrates an assortment of deductive and instructive tools, techniques, and technologies to arm professionals with the tools they need to be prepared and stay ahead of the game.

Step-by-step instructions

The book is a practical guide on how to conduct forensic investigations using self-organizing clustering map (SOM) neural networks, text extraction, and rule generating software to "interrogate the evidence." This powerful data is indispensable for fraud detection, cybersecurity, competitive counterintelligence, and corporate and litigation investigations. The book also provides step-by-step instructions on how to construct adaptive criminal and fraud detection systems for organizations.

Prediction is the key

Internet activity, email, and wireless communications can be captured, modeled, and deployed in order to anticipate potential cyber attacks and other types of crimes. The successful prediction of human reactions and server actions by quantifying their behaviors is invaluable for pre-empting criminal activity. This volume assists chief information officers, law enforcement personnel, legal and IT professionals, investigators, and competitive intelligence analysts in the strategic planning needed to recognize the patterns of criminal activities in order to predict when and where crimes and intrusions are likely to take place.

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire. Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users. Examines the construction and leveraging of mobile sites Describes how to use mobile apps to gather key data about consumers' behavior and preferences Discusses mobile mobs, which can be differentiated as distinct marketplaces-including Apple (R), Google (R), Facebook (R), Amazon (R), and Twitter (R) Provides detailed coverage of mobile analytics via clustering, text, and classification AI software and techniques Mobile devices serve as detailed diaries of a person, continuously and intimately broadcasting where, how, when, and what products, services, and content your consumers desire. The future is mobile-data mining starts and stops in consumers' pockets. Describing how to analyze Wi-Fi and GPS data from websites and apps, the book explains how to model mined data through the use of artificial intelligence software. It also discusses the monetization of mobile devices' desires and preferences that can lead to the triangulated marketing of content, products, or services to billions of consumers-in a relevant, anonymous, and personal manner.

In today s wireless environment, marketing is more frequently occurring at the server-to-device level with that device being anything from a laptop or phone to a TV or car. In this real-time digital marketplace, human attributes such as income, marital status, and age are not the most reliable attributes for modeling consumer behaviors. A more effective approach is to monitor and model the consumer s device activities and behavioral patterns. Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere Anytime (A5) examines the technologies, software, networks, mechanisms, techniques, and solution providers that are shaping the next generation of mobile advertising. Discussing the interactive environments that comprise the web, it explains how to deploy Machine-to-Machine Marketing (M3) and Anonymous Advertising Apps Anywhere Anytime (A5). The book is organized into four sections: Why Discusses the interactive environments and explains how M3 can be deployed How Describes which technologies and solution providers can be used for executing M3 Checklists Contains lists of techniques, strategies, technologies, and solution providers for M3 Case Studies Illustrates M3 and A5 implementations in companies across various industries Providing wide-ranging coverage that touches on data mining, the web, social media, marketing, and mobile communications, the book s case studies show how M3 and A5 are being implemented at JP Morgan Chase, Hyundai, Dunkin Donuts, New York Life, Twitter, Best Buy, JetBlue, IKEA, Urban Outfitters, JC Penney, Sony, eHarmony, and NASCAR just to name a few. These case studies provide you with the real-world insight needed to market effectively and profitably well into the future. Each company, network, and resourc

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire. Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users. Examines the construction and leveraging of mobile sites Describes how to use mobile apps to gather key data about consumers' behavior and preferences Discusses mobile mobs, which can be differentiated as distinct marketplaces-including Apple (R), Google (R), Facebook (R), Amazon (R), and Twitter (R) Provides detailed coverage of mobile analytics via clustering, text, and classification AI software and techniques Mobile devices serve as detailed diaries of a person, continuously and intimately broadcasting where, how, when, and what products, services, and content your consumers desire. The future is mobile-data mining starts and stops in consumers' pockets. Describing how to analyze Wi-Fi and GPS data from websites and apps, the book explains how to model mined data through the use of artificial intelligence software. It also discusses the monetization of mobile devices' desires and preferences that can lead to the triangulated marketing of content, products, or services to billions of consumers-in a relevant, anonymous, and personal manner.